Dataset: 
All our experiments use a new large synthetic NeRF dataset of shapes from 13 categories (that overlap with [1]}, [2]}, [3]}) from the ShapeNet [4]} dataset.
For each category, we pick 100 instances from ShapeNet, rotate the shape randomly, and render 54 omnidirectional views sampled in a cube around the shape using Blender.
Different from other datasets, we also simulate cluttered background.
These posed views are used to train 100 NeRF models per category using the public PyTorch implementation of NeRF [5]} for a total of 1300 NeRF models.
To our knowledge, this is the one of the largest (compared to [6]}, [7]}) 360\(^\circ \)  synthetic NeRF datasets – we will publicly release the simulator, raw posed images, and trained NeRF weights.
Each NeRF model is trained for 400 epochs with 1024 randomly selected rays per iteration, a coarse sampling resolution of 64 points, and a fine sampling resolution of 128 points along each ray.
Rather than train NeRF to maximize PSNR on a novel view, we fix the number of epochs for all models to enable fair comparison between instances.
As a consequence, some NeRF models may have a lower PSNR, but our goal is not to increase NeRF quality but rather improve canonicalization quality – hence we fix the number of epochs.
For each category, we set aside 20% of the models for testing.
